Overview

Dataset statistics

Number of variables20
Number of observations295653
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Time (s) is highly overall correlated with CO (ppm) and 3 other fieldsHigh correlation
CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 10 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 9 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 6 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 6 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with Time (s) and 12 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 3 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -112.6102791)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32172 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:28:34.732770
Analysis finished2022-12-20 08:30:23.882599
Duration1 minute and 49.15 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295653
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45454.137
Minimum0
Maximum90909.727
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:24.056553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4547.266
Q122735.994
median45449.712
Q368183.384
95-th percentile86358.194
Maximum90909.727
Range90909.727
Interquartile range (IQR)45447.39

Descriptive statistics

Standard deviation26242.664
Coefficient of variation (CV)0.57734379
Kurtosis-1.1999842
Mean45454.137
Median Absolute Deviation (MAD)22723.917
Skewness-0.00027105822
Sum1.3438652 × 1010
Variance6.8867739 × 108
MonotonicityStrictly increasing
2022-12-20T14:00:24.347810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60639.218 1
 
< 0.1%
60610.79 1
 
< 0.1%
60610.481 1
 
< 0.1%
60610.173 1
 
< 0.1%
60609.864 1
 
< 0.1%
60609.554 1
 
< 0.1%
60609.245 1
 
< 0.1%
60608.935 1
 
< 0.1%
60608.626 1
 
< 0.1%
Other values (295643) 295643
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.31 1
< 0.1%
0.62 1
< 0.1%
0.929 1
< 0.1%
1.237 1
< 0.1%
1.546 1
< 0.1%
1.857 1
< 0.1%
2.164 1
< 0.1%
2.474 1
< 0.1%
2.784 1
< 0.1%
ValueCountFrequency (%)
90909.727 1
< 0.1%
90909.417 1
< 0.1%
90909.107 1
< 0.1%
90908.799 1
< 0.1%
90908.491 1
< 0.1%
90908.182 1
< 0.1%
90907.874 1
< 0.1%
90907.564 1
< 0.1%
90907.255 1
< 0.1%
90906.945 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct303
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8988159
Minimum0
Maximum20
Zeros32172
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:24.615396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4270686
Coefficient of variation (CV)0.64927651
Kurtosis-1.2330873
Mean9.8988159
Median Absolute Deviation (MAD)6.67
Skewness0.0095385244
Sum2926614.6
Variance41.307211
MonotonicityNot monotonic
2022-12-20T14:00:24.774048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32172
10.9%
4.44 29287
9.9%
13.33 29275
9.9%
6.67 29265
9.9%
20 29245
9.9%
15.56 29235
9.9%
2.22 29229
9.9%
11.11 29229
9.9%
8.89 29222
9.9%
17.78 29199
9.9%
Other values (293) 295
 
0.1%
ValueCountFrequency (%)
0 32172
10.9%
0.242 1
 
< 0.1%
0.2797 1
 
< 0.1%
0.2864 1
 
< 0.1%
0.3289 1
 
< 0.1%
0.444 1
 
< 0.1%
0.4936 1
 
< 0.1%
0.78 1
 
< 0.1%
0.9246 1
 
< 0.1%
0.9302 1
 
< 0.1%
ValueCountFrequency (%)
20 29245
9.9%
19.9467 1
 
< 0.1%
19.6537 1
 
< 0.1%
19.596 1
 
< 0.1%
18.91 1
 
< 0.1%
18.8367 1
 
< 0.1%
18.8265 1
 
< 0.1%
18.2995 1
 
< 0.1%
18.2262 1
 
< 0.1%
18.1871 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct21316
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.074856
Minimum16.83
Maximum70.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:24.944331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.83
5-th percentile22.94
Q135.74
median45.97
Q354.32
95-th percentile63.98
Maximum70.39
Range53.56
Interquartile range (IQR)18.58

Descriptive statistics

Standard deviation12.0645
Coefficient of variation (CV)0.26765477
Kurtosis-0.74397395
Mean45.074856
Median Absolute Deviation (MAD)9.16
Skewness-0.1585812
Sum13326517
Variance145.55217
MonotonicityNot monotonic
2022-12-20T14:00:25.106072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.59 3002
 
1.0%
35.7 2091
 
0.7%
32.59 1665
 
0.6%
47.52 1621
 
0.5%
38.12 1434
 
0.5%
37.81 1432
 
0.5%
22.85 1395
 
0.5%
29.3 1314
 
0.4%
43.37 1281
 
0.4%
46.48 1258
 
0.4%
Other values (21306) 279160
94.4%
ValueCountFrequency (%)
16.83 160
 
0.1%
16.8955 1
 
< 0.1%
16.9885 1
 
< 0.1%
17.0658 1
 
< 0.1%
17.1615 1
 
< 0.1%
17.2366 1
 
< 0.1%
17.334 1
 
< 0.1%
17.39 1049
0.4%
17.4762 1
 
< 0.1%
17.5537 1
 
< 0.1%
ValueCountFrequency (%)
70.39 39
< 0.1%
70.3896 1
 
< 0.1%
70.3888 1
 
< 0.1%
70.3884 1
 
< 0.1%
70.3883 1
 
< 0.1%
70.388 1
 
< 0.1%
70.3872 1
 
< 0.1%
70.3865 1
 
< 0.1%
70.3857 1
 
< 0.1%
70.3853 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct9601
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.818018
Minimum21.86
Maximum25.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:25.265809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21.86
5-th percentile21.98
Q122.58
median23.82
Q325.1
95-th percentile25.5
Maximum25.66
Range3.8
Interquartile range (IQR)2.52

Descriptive statistics

Standard deviation1.2856993
Coefficient of variation (CV)0.053980113
Kurtosis-1.5913522
Mean23.818018
Median Absolute Deviation (MAD)1.28
Skewness-0.085295626
Sum7041868.5
Variance1.6530228
MonotonicityNot monotonic
2022-12-20T14:00:25.419379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.1 17375
 
5.9%
25.18 16543
 
5.6%
25.14 8749
 
3.0%
21.98 7336
 
2.5%
21.9 7326
 
2.5%
25.06 7247
 
2.5%
25.26 7041
 
2.4%
23.66 6858
 
2.3%
25.5 6777
 
2.3%
25.58 6525
 
2.2%
Other values (9591) 203876
69.0%
ValueCountFrequency (%)
21.86 591
0.2%
21.8601 48
 
< 0.1%
21.8602 1
 
< 0.1%
21.8608 1
 
< 0.1%
21.8609 2
 
< 0.1%
21.861 1
 
< 0.1%
21.8612 1
 
< 0.1%
21.8613 1
 
< 0.1%
21.8615 1
 
< 0.1%
21.8616 3
 
< 0.1%
ValueCountFrequency (%)
25.66 139
< 0.1%
25.6584 1
 
< 0.1%
25.6506 2
 
< 0.1%
25.6488 2
 
< 0.1%
25.6482 1
 
< 0.1%
25.6463 1
 
< 0.1%
25.6385 1
 
< 0.1%
25.6384 1
 
< 0.1%
25.6366 2
 
< 0.1%
25.6361 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11076
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.95223
Minimum0
Maximum314.4902
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:25.586063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.76136
Q1239.8974
median239.9726
Q3240.0441
95-th percentile240.1747
Maximum314.4902
Range314.4902
Interquartile range (IQR)0.1467

Descriptive statistics

Standard deviation1.7570578
Coefficient of variation (CV)0.0073225317
Kurtosis14869.224
Mean239.95223
Median Absolute Deviation (MAD)0.0733
Skewness-112.61028
Sum70942597
Variance3.0872522
MonotonicityNot monotonic
2022-12-20T14:00:25.737773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9684 151
 
0.1%
239.9794 146
 
< 0.1%
239.9676 145
 
< 0.1%
239.9878 141
 
< 0.1%
239.9763 140
 
< 0.1%
240.0085 139
 
< 0.1%
239.979 136
 
< 0.1%
239.9835 135
 
< 0.1%
239.9772 135
 
< 0.1%
239.9574 134
 
< 0.1%
Other values (11066) 294251
99.5%
ValueCountFrequency (%)
0 9
< 0.1%
0.1994 1
 
< 0.1%
0.4217 1
 
< 0.1%
0.6448 1
 
< 0.1%
49.273 1
 
< 0.1%
118.1436 1
 
< 0.1%
123.4182 1
 
< 0.1%
125.3341 1
 
< 0.1%
160.4938 1
 
< 0.1%
168.5489 1
 
< 0.1%
ValueCountFrequency (%)
314.4902 1
< 0.1%
311.2264 1
< 0.1%
288.3779 1
< 0.1%
285.9962 1
< 0.1%
276.6573 1
< 0.1%
267.5351 1
< 0.1%
265.9228 1
< 0.1%
264.6174 1
< 0.1%
262.2656 1
< 0.1%
260.7659 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1765
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35563159
Minimum0.199
Maximum0.9026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:25.901482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.199
5-th percentile0.2
Q10.2
median0.2005
Q30.2073
95-th percentile0.9005
Maximum0.9026
Range0.7036
Interquartile range (IQR)0.0073

Descriptive statistics

Standard deviation0.288957
Coefficient of variation (CV)0.8125178
Kurtosis-0.20478606
Mean0.35563159
Median Absolute Deviation (MAD)0.0005
Skewness1.3374698
Sum105143.55
Variance0.083496146
MonotonicityNot monotonic
2022-12-20T14:00:26.056894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 117688
39.8%
0.201 25627
 
8.7%
0.9 15561
 
5.3%
0.901 9728
 
3.3%
0.2009 6467
 
2.2%
0.2007 6435
 
2.2%
0.2005 6409
 
2.2%
0.2002 6409
 
2.2%
0.2003 6401
 
2.2%
0.2001 6384
 
2.2%
Other values (1755) 88544
29.9%
ValueCountFrequency (%)
0.199 71
 
< 0.1%
0.1991 151
0.1%
0.1992 152
0.1%
0.1993 188
0.1%
0.1994 159
0.1%
0.1995 163
0.1%
0.1996 171
0.1%
0.1997 153
0.1%
0.1998 137
< 0.1%
0.1999 151
0.1%
ValueCountFrequency (%)
0.9026 1
 
< 0.1%
0.902 75
< 0.1%
0.9019 88
< 0.1%
0.9018 98
< 0.1%
0.9017 82
< 0.1%
0.9016 91
< 0.1%
0.9015 80
< 0.1%
0.9014 111
< 0.1%
0.9013 93
< 0.1%
0.9012 93
< 0.1%

R1 (MOhm)
Real number (ℝ)

Distinct8475
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.32394
Minimum0.0331
Maximum124.4448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:26.222005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.0792
Q10.4909
median3.8179
Q337.8193
95-th percentile77.677
Maximum124.4448
Range124.4117
Interquartile range (IQR)37.3284

Descriptive statistics

Standard deviation27.112637
Coefficient of variation (CV)1.3340246
Kurtosis0.38816371
Mean20.32394
Median Absolute Deviation (MAD)3.7364
Skewness1.2425191
Sum6008834
Variance735.09508
MonotonicityNot monotonic
2022-12-20T14:00:26.388733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.3332 756
 
0.3%
75.6194 754
 
0.3%
74.3444 750
 
0.3%
73.7788 741
 
0.3%
75.0345 740
 
0.3%
76.9383 737
 
0.2%
72.5638 716
 
0.2%
79.0683 707
 
0.2%
77.677 698
 
0.2%
70.7619 690
 
0.2%
Other values (8465) 288364
97.5%
ValueCountFrequency (%)
0.0331 2
< 0.1%
0.0334 1
 
< 0.1%
0.0338 2
< 0.1%
0.0339 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0344 3
< 0.1%
0.0345 1
 
< 0.1%
0.0347 1
 
< 0.1%
0.0348 1
 
< 0.1%
0.0349 2
< 0.1%
ValueCountFrequency (%)
124.4448 1
 
< 0.1%
122.8846 4
 
< 0.1%
121.0628 10
 
< 0.1%
119.5851 12
 
< 0.1%
117.8584 18
 
< 0.1%
116.4568 28
 
< 0.1%
114.818 48
< 0.1%
113.4868 49
< 0.1%
111.9292 78
< 0.1%
110.6632 78
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8223
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.093633
Minimum0.0569
Maximum176.2282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:26.553694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0569
5-th percentile0.1415
Q10.5538
median2.5434
Q343.9904
95-th percentile85.1632
Maximum176.2282
Range176.1713
Interquartile range (IQR)43.4366

Descriptive statistics

Standard deviation30.343075
Coefficient of variation (CV)1.3733855
Kurtosis-0.15670493
Mean22.093633
Median Absolute Deviation (MAD)2.4014
Skewness1.1428093
Sum6532048.9
Variance920.70222
MonotonicityNot monotonic
2022-12-20T14:00:26.817641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.5541 1173
 
0.4%
84.278 1163
 
0.4%
82.004 1160
 
0.4%
82.7015 1154
 
0.4%
80.5097 1127
 
0.4%
85.1632 1094
 
0.4%
88.5723 1069
 
0.4%
79.7171 1064
 
0.4%
86.8347 1049
 
0.4%
81.1822 1045
 
0.4%
Other values (8213) 284555
96.2%
ValueCountFrequency (%)
0.0569 1
< 0.1%
0.0584 2
< 0.1%
0.0592 1
< 0.1%
0.0594 1
< 0.1%
0.0596 2
< 0.1%
0.06 2
< 0.1%
0.0601 1
< 0.1%
0.0603 1
< 0.1%
0.0608 1
< 0.1%
0.0616 2
< 0.1%
ValueCountFrequency (%)
176.2282 1
 
< 0.1%
147.2183 1
 
< 0.1%
142.5199 1
 
< 0.1%
138.1101 1
 
< 0.1%
133.9633 1
 
< 0.1%
130.0566 2
< 0.1%
128.0195 1
 
< 0.1%
124.4448 1
 
< 0.1%
122.8846 2
< 0.1%
121.0628 3
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8154
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.737034
Minimum0.0572
Maximum165.1174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:26.986534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0572
5-th percentile0.1145
Q10.7331
median9.6164
Q355.5168
95-th percentile88.0388
Maximum165.1174
Range165.0602
Interquartile range (IQR)54.7837

Descriptive statistics

Standard deviation31.733639
Coefficient of variation (CV)1.1440891
Kurtosis-0.81727079
Mean27.737034
Median Absolute Deviation (MAD)9.499
Skewness0.79046149
Sum8200537.4
Variance1007.0238
MonotonicityNot monotonic
2022-12-20T14:00:27.136727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.3116 1244
 
0.4%
84.6501 1228
 
0.4%
85.3974 1223
 
0.4%
87.0883 1222
 
0.4%
89.8357 1218
 
0.4%
88.0388 1210
 
0.4%
88.8467 1199
 
0.4%
90.6767 1188
 
0.4%
92.5828 1165
 
0.4%
91.7066 1152
 
0.4%
Other values (8144) 283604
95.9%
ValueCountFrequency (%)
0.0572 1
< 0.1%
0.0578 1
< 0.1%
0.0584 1
< 0.1%
0.0586 1
< 0.1%
0.0589 1
< 0.1%
0.0597 1
< 0.1%
0.0598 1
< 0.1%
0.06 2
< 0.1%
0.0605 1
< 0.1%
0.0608 2
< 0.1%
ValueCountFrequency (%)
165.1174 1
 
< 0.1%
118.8647 3
 
< 0.1%
117.1484 1
 
< 0.1%
115.7553 5
 
< 0.1%
114.1263 24
 
< 0.1%
112.8031 60
 
< 0.1%
111.2549 80
 
< 0.1%
109.9965 144
< 0.1%
108.5233 139
< 0.1%
107.3251 209
0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7310
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.585834
Minimum0.0297
Maximum112.0863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:27.303350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0297
5-th percentile0.1038
Q13.1532
median26.8327
Q338.1853
95-th percentile54.8767
Maximum112.0863
Range112.0566
Interquartile range (IQR)35.0321

Descriptive statistics

Standard deviation18.769558
Coefficient of variation (CV)0.7634298
Kurtosis-0.97120492
Mean24.585834
Median Absolute Deviation (MAD)15.2716
Skewness0.13608765
Sum7268875.5
Variance352.29631
MonotonicityNot monotonic
2022-12-20T14:00:27.463035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.618 1260
 
0.4%
38.1853 1257
 
0.4%
36.4987 1251
 
0.4%
38.0062 1248
 
0.4%
36.6641 1239
 
0.4%
35.4426 1225
 
0.4%
37.0331 1222
 
0.4%
35.9471 1216
 
0.4%
37.2375 1215
 
0.4%
37.4095 1214
 
0.4%
Other values (7300) 283306
95.8%
ValueCountFrequency (%)
0.0297 1
 
< 0.1%
0.0418 1
 
< 0.1%
0.042 1
 
< 0.1%
0.0426 1
 
< 0.1%
0.0427 2
< 0.1%
0.0429 1
 
< 0.1%
0.0431 1
 
< 0.1%
0.0432 3
< 0.1%
0.0433 1
 
< 0.1%
0.0435 1
 
< 0.1%
ValueCountFrequency (%)
112.0863 1
 
< 0.1%
91.8226 1
 
< 0.1%
86.4726 1
 
< 0.1%
82.6836 2
 
< 0.1%
81.8678 2
 
< 0.1%
80.9098 6
 
< 0.1%
80.1281 11
 
< 0.1%
79.2097 28
< 0.1%
78.4601 26
< 0.1%
77.5789 41
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7559
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.076474
Minimum0.0508
Maximum208.7106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:27.634222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0508
5-th percentile0.1182
Q12.8171
median43.2625
Q359.9876
95-th percentile87.4402
Maximum208.7106
Range208.6598
Interquartile range (IQR)57.1705

Descriptive statistics

Standard deviation30.060489
Coefficient of variation (CV)0.78947671
Kurtosis-1.0898682
Mean38.076474
Median Absolute Deviation (MAD)25.7435
Skewness0.1386466
Sum11257424
Variance903.633
MonotonicityNot monotonic
2022-12-20T14:00:27.794205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.7412 1785
 
0.6%
56.1324 1734
 
0.6%
57.2027 1721
 
0.6%
56.4626 1707
 
0.6%
56.864 1702
 
0.6%
57.9621 1702
 
0.6%
55.0376 1699
 
0.6%
58.3847 1690
 
0.6%
52.1043 1681
 
0.6%
54.3512 1678
 
0.6%
Other values (7549) 278554
94.2%
ValueCountFrequency (%)
0.0508 1
 
< 0.1%
0.0511 1
 
< 0.1%
0.0512 1
 
< 0.1%
0.0513 3
< 0.1%
0.0515 1
 
< 0.1%
0.0517 1
 
< 0.1%
0.0519 1
 
< 0.1%
0.052 1
 
< 0.1%
0.0522 1
 
< 0.1%
0.0528 1
 
< 0.1%
ValueCountFrequency (%)
208.7106 1
 
< 0.1%
179.0785 1
 
< 0.1%
135.5445 2
 
< 0.1%
131.5393 4
 
< 0.1%
129.7955 4
 
< 0.1%
127.7625 11
 
< 0.1%
126.116 10
 
< 0.1%
124.1949 18
< 0.1%
122.6378 26
< 0.1%
120.8197 34
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7619
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.842493
Minimum0.0495
Maximum145.1587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:27.959444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0495
5-th percentile0.1262
Q12.2796
median35.1933
Q357.649
95-th percentile86.6118
Maximum145.1587
Range145.1092
Interquartile range (IQR)55.3694

Descriptive statistics

Standard deviation29.927009
Coefficient of variation (CV)0.85892273
Kurtosis-1.0717316
Mean34.842493
Median Absolute Deviation (MAD)28.1518
Skewness0.3304101
Sum10301287
Variance895.62587
MonotonicityNot monotonic
2022-12-20T14:00:28.123930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.0078 1543
 
0.5%
54.1947 1533
 
0.5%
55.6631 1526
 
0.5%
53.8074 1504
 
0.5%
53.4887 1499
 
0.5%
57.2118 1493
 
0.5%
56.427 1487
 
0.5%
54.5217 1477
 
0.5%
54.9192 1469
 
0.5%
55.2549 1468
 
0.5%
Other values (7609) 280654
94.9%
ValueCountFrequency (%)
0.0495 3
< 0.1%
0.0497 1
 
< 0.1%
0.0504 1
 
< 0.1%
0.0508 1
 
< 0.1%
0.0512 1
 
< 0.1%
0.0513 1
 
< 0.1%
0.0516 1
 
< 0.1%
0.0518 1
 
< 0.1%
0.0519 2
< 0.1%
0.0521 1
 
< 0.1%
ValueCountFrequency (%)
145.1587 1
 
< 0.1%
138.2019 1
 
< 0.1%
125.7588 1
 
< 0.1%
123.7317 1
 
< 0.1%
122.0913 2
 
< 0.1%
120.179 6
 
< 0.1%
118.6302 6
 
< 0.1%
116.8232 20
< 0.1%
115.3585 29
< 0.1%
113.6483 42
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7508
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.202328
Minimum0.0539
Maximum181.975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:28.298898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0539
5-th percentile0.1252
Q12.953
median43.0013
Q360.4548
95-th percentile87.8609
Maximum181.975
Range181.9211
Interquartile range (IQR)57.5018

Descriptive statistics

Standard deviation30.282518
Coefficient of variation (CV)0.79268777
Kurtosis-1.1516415
Mean38.202328
Median Absolute Deviation (MAD)26.5273
Skewness0.13554449
Sum11294633
Variance917.03091
MonotonicityNot monotonic
2022-12-20T14:00:28.459512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.1218 1767
 
0.6%
56.8574 1760
 
0.6%
55.7936 1725
 
0.6%
56.5207 1711
 
0.6%
57.2667 1705
 
0.6%
58.0323 1705
 
0.6%
57.6122 1703
 
0.6%
58.387 1695
 
0.6%
58.8183 1690
 
0.6%
59.6255 1684
 
0.6%
Other values (7498) 278508
94.2%
ValueCountFrequency (%)
0.0539 1
< 0.1%
0.0546 2
< 0.1%
0.0549 1
< 0.1%
0.055 1
< 0.1%
0.0553 1
< 0.1%
0.0556 2
< 0.1%
0.0557 1
< 0.1%
0.0566 1
< 0.1%
0.0567 1
< 0.1%
0.0572 1
< 0.1%
ValueCountFrequency (%)
181.975 1
 
< 0.1%
153.387 1
 
< 0.1%
151.012 1
 
< 0.1%
141.3751 2
 
< 0.1%
137.0008 2
 
< 0.1%
121.8976 1
 
< 0.1%
120.0904 1
 
< 0.1%
118.6246 5
 
< 0.1%
116.9118 12
< 0.1%
115.5214 17
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6162
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.125919
Minimum0.0344
Maximum106.9859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:28.636094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0344
5-th percentile0.1007
Q113.0332
median30.4421
Q348.5445
95-th percentile70.1254
Maximum106.9859
Range106.9515
Interquartile range (IQR)35.5113

Descriptive statistics

Standard deviation22.982933
Coefficient of variation (CV)0.73838568
Kurtosis-0.89876504
Mean31.125919
Median Absolute Deviation (MAD)18.0344
Skewness0.16678348
Sum9202471.3
Variance528.21519
MonotonicityNot monotonic
2022-12-20T14:00:28.800617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.0412 1345
 
0.5%
60.1664 1319
 
0.4%
57.5888 1318
 
0.4%
58.8889 1286
 
0.4%
56.7773 1281
 
0.4%
55.988 1274
 
0.4%
56.4158 1268
 
0.4%
59.7614 1264
 
0.4%
58.4235 1259
 
0.4%
54.4724 1248
 
0.4%
Other values (6152) 282791
95.6%
ValueCountFrequency (%)
0.0344 1
 
< 0.1%
0.0346 2
< 0.1%
0.0348 2
< 0.1%
0.0352 1
 
< 0.1%
0.0355 3
< 0.1%
0.0356 1
 
< 0.1%
0.0357 1
 
< 0.1%
0.0358 4
< 0.1%
0.0359 2
< 0.1%
0.036 1
 
< 0.1%
ValueCountFrequency (%)
106.9859 2
 
< 0.1%
105.4803 1
 
< 0.1%
104.2574 4
 
< 0.1%
102.8265 3
 
< 0.1%
101.6635 4
 
< 0.1%
100.3018 5
 
< 0.1%
99.1944 8
 
< 0.1%
97.8971 10
< 0.1%
96.8414 18
< 0.1%
95.604 22
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6139
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.880526
Minimum0.0298
Maximum107.9036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:29.077956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0298
5-th percentile0.0973
Q19.2227
median25.4465
Q344.5381
95-th percentile66.7926
Maximum107.9036
Range107.8738
Interquartile range (IQR)35.3154

Descriptive statistics

Standard deviation21.942505
Coefficient of variation (CV)0.78701906
Kurtosis-0.85955622
Mean27.880526
Median Absolute Deviation (MAD)18.4031
Skewness0.34886706
Sum8242961.2
Variance481.47354
MonotonicityNot monotonic
2022-12-20T14:00:29.237811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0977 2566
 
0.9%
0.0976 2458
 
0.8%
0.0978 2337
 
0.8%
0.0975 2271
 
0.8%
0.0973 2234
 
0.8%
0.0981 2145
 
0.7%
0.098 2105
 
0.7%
0.0982 2059
 
0.7%
0.0972 2038
 
0.7%
0.0987 2001
 
0.7%
Other values (6129) 273439
92.5%
ValueCountFrequency (%)
0.0298 1
 
< 0.1%
0.0299 1
 
< 0.1%
0.03 1
 
< 0.1%
0.0301 4
< 0.1%
0.0302 4
< 0.1%
0.0303 3
< 0.1%
0.0306 3
< 0.1%
0.0307 3
< 0.1%
0.0308 6
< 0.1%
0.0309 5
< 0.1%
ValueCountFrequency (%)
107.9036 1
 
< 0.1%
95.6857 1
 
< 0.1%
92.4455 1
 
< 0.1%
91.5335 1
 
< 0.1%
88.5615 4
 
< 0.1%
87.7234 9
 
< 0.1%
86.7382 18
 
< 0.1%
85.9337 29
< 0.1%
84.9877 48
< 0.1%
84.2149 72
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6388
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.957747
Minimum0.0372
Maximum139.2366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:29.401837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0372
5-th percentile0.1188
Q17.734
median25.5373
Q347.0683
95-th percentile71.4844
Maximum139.2366
Range139.1994
Interquartile range (IQR)39.3343

Descriptive statistics

Standard deviation23.691406
Coefficient of variation (CV)0.81813704
Kurtosis-0.85159397
Mean28.957747
Median Absolute Deviation (MAD)20.7958
Skewness0.42934871
Sum8561444.8
Variance561.28271
MonotonicityNot monotonic
2022-12-20T14:00:29.559125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1221 1521
 
0.5%
0.1223 1485
 
0.5%
0.1204 1477
 
0.5%
0.1217 1473
 
0.5%
0.1199 1434
 
0.5%
0.1206 1430
 
0.5%
0.1208 1425
 
0.5%
0.1215 1417
 
0.5%
0.1216 1411
 
0.5%
0.1201 1402
 
0.5%
Other values (6378) 281178
95.1%
ValueCountFrequency (%)
0.0372 1
 
< 0.1%
0.0374 1
 
< 0.1%
0.038 1
 
< 0.1%
0.0384 1
 
< 0.1%
0.0385 1
 
< 0.1%
0.0386 2
 
< 0.1%
0.0387 2
 
< 0.1%
0.0388 5
< 0.1%
0.039 2
 
< 0.1%
0.0391 1
 
< 0.1%
ValueCountFrequency (%)
139.2366 1
 
< 0.1%
137.2782 1
 
< 0.1%
107.3251 1
 
< 0.1%
102.3503 5
 
< 0.1%
101.0719 4
 
< 0.1%
100.0304 5
 
< 0.1%
98.8084 10
< 0.1%
97.8124 9
< 0.1%
96.6431 14
< 0.1%
95.6897 18
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6099
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.918622
Minimum0.0316
Maximum116.1061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:29.732249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0316
5-th percentile0.1085
Q111.1345
median29.374
Q348.8064
95-th percentile72.3453
Maximum116.1061
Range116.0745
Interquartile range (IQR)37.6719

Descriptive statistics

Standard deviation23.677764
Coefficient of variation (CV)0.76580918
Kurtosis-0.86445053
Mean30.918622
Median Absolute Deviation (MAD)19.4309
Skewness0.27901655
Sum9141183.3
Variance560.63653
MonotonicityNot monotonic
2022-12-20T14:00:29.896284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1101 2012
 
0.7%
0.1092 1978
 
0.7%
0.1089 1963
 
0.7%
0.1085 1958
 
0.7%
0.11 1954
 
0.7%
0.1086 1941
 
0.7%
0.1088 1933
 
0.7%
0.109 1865
 
0.6%
0.1104 1818
 
0.6%
0.1102 1813
 
0.6%
Other values (6089) 276418
93.5%
ValueCountFrequency (%)
0.0316 1
 
< 0.1%
0.0317 2
< 0.1%
0.032 1
 
< 0.1%
0.0323 2
< 0.1%
0.0324 3
< 0.1%
0.0325 2
< 0.1%
0.0326 4
< 0.1%
0.0327 1
 
< 0.1%
0.0328 4
< 0.1%
0.033 1
 
< 0.1%
ValueCountFrequency (%)
116.1061 1
 
< 0.1%
108.8521 1
 
< 0.1%
107.6503 1
 
< 0.1%
101.3781 2
 
< 0.1%
100.3335 6
 
< 0.1%
99.1078 11
 
< 0.1%
98.1088 18
< 0.1%
96.936 29
< 0.1%
95.9797 30
< 0.1%
94.8564 43
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6212
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.864356
Minimum0.0331
Maximum99.3074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:30.068851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.108
Q110.5802
median29.4331
Q347.589
95-th percentile65.9633
Maximum99.3074
Range99.2743
Interquartile range (IQR)37.0088

Descriptive statistics

Standard deviation22.150393
Coefficient of variation (CV)0.7417
Kurtosis-0.92987412
Mean29.864356
Median Absolute Deviation (MAD)18.1559
Skewness0.15328449
Sum8829486.6
Variance490.63992
MonotonicityNot monotonic
2022-12-20T14:00:30.233050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.7888 1331
 
0.5%
54.7785 1330
 
0.4%
54.4059 1303
 
0.4%
56.1889 1301
 
0.4%
55.0929 1272
 
0.4%
56.9212 1267
 
0.4%
56.5195 1265
 
0.4%
54.0991 1265
 
0.4%
58.0204 1263
 
0.4%
55.4749 1263
 
0.4%
Other values (6202) 282793
95.7%
ValueCountFrequency (%)
0.0331 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0338 2
< 0.1%
0.034 1
 
< 0.1%
0.0341 2
< 0.1%
0.0343 1
 
< 0.1%
0.0347 2
< 0.1%
0.0349 3
< 0.1%
0.035 1
 
< 0.1%
ValueCountFrequency (%)
99.3074 1
 
< 0.1%
98.3064 1
 
< 0.1%
97.1312 5
 
< 0.1%
96.173 8
 
< 0.1%
95.0475 16
 
< 0.1%
94.1293 27
< 0.1%
93.0504 27
< 0.1%
92.1698 53
< 0.1%
91.1346 60
< 0.1%
90.2894 67
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6322
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.397769
Minimum0.0345
Maximum93.0887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:30.397804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0345
5-th percentile0.1019
Q18.4269
median24.6277
Q341.9854
95-th percentile62.4181
Maximum93.0887
Range93.0542
Interquartile range (IQR)33.5585

Descriptive statistics

Standard deviation20.62697
Coefficient of variation (CV)0.78139067
Kurtosis-0.83441811
Mean26.397769
Median Absolute Deviation (MAD)17.1337
Skewness0.33273225
Sum7804579.5
Variance425.4719
MonotonicityNot monotonic
2022-12-20T14:00:30.555038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1034 1088
 
0.4%
0.1037 1024
 
0.3%
0.1044 1024
 
0.3%
0.1042 1024
 
0.3%
0.1032 1020
 
0.3%
0.1043 1016
 
0.3%
48.1178 1013
 
0.3%
0.1046 1012
 
0.3%
0.103 1007
 
0.3%
52.4943 998
 
0.3%
Other values (6312) 285427
96.5%
ValueCountFrequency (%)
0.0345 1
 
< 0.1%
0.035 1
 
< 0.1%
0.0351 1
 
< 0.1%
0.0353 1
 
< 0.1%
0.0355 2
< 0.1%
0.0357 1
 
< 0.1%
0.0358 1
 
< 0.1%
0.036 1
 
< 0.1%
0.0362 1
 
< 0.1%
0.0363 4
< 0.1%
ValueCountFrequency (%)
93.0887 1
 
< 0.1%
92.0217 1
 
< 0.1%
86.5605 2
 
< 0.1%
85.7885 2
 
< 0.1%
84.8799 8
 
< 0.1%
84.1371 10
 
< 0.1%
83.2626 20
 
< 0.1%
82.5474 30
< 0.1%
81.7051 52
< 0.1%
81.016 71
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6194
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.390579
Minimum0.0319
Maximum129.422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:00:30.713681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0319
5-th percentile0.1077
Q110.2427
median29.9714
Q352.1742
95-th percentile76.2732
Maximum129.422
Range129.3901
Interquartile range (IQR)41.9315

Descriptive statistics

Standard deviation25.243233
Coefficient of variation (CV)0.77933875
Kurtosis-1.0030923
Mean32.390579
Median Absolute Deviation (MAD)21.5748
Skewness0.28105919
Sum9576371.9
Variance637.22083
MonotonicityNot monotonic
2022-12-20T14:00:30.883231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1082 1979
 
0.7%
0.1084 1925
 
0.7%
0.1086 1921
 
0.6%
0.1083 1898
 
0.6%
0.1078 1866
 
0.6%
0.108 1853
 
0.6%
0.1077 1845
 
0.6%
0.1079 1826
 
0.6%
0.1091 1809
 
0.6%
0.1087 1797
 
0.6%
Other values (6184) 276934
93.7%
ValueCountFrequency (%)
0.0319 1
 
< 0.1%
0.0322 1
 
< 0.1%
0.0325 1
 
< 0.1%
0.0326 2
< 0.1%
0.0327 1
 
< 0.1%
0.0328 2
< 0.1%
0.0329 1
 
< 0.1%
0.033 1
 
< 0.1%
0.0332 4
< 0.1%
0.0333 2
< 0.1%
ValueCountFrequency (%)
129.422 1
 
< 0.1%
114.3522 1
 
< 0.1%
104.6376 1
 
< 0.1%
102.1866 1
 
< 0.1%
99.8467 2
 
< 0.1%
97.6107 1
 
< 0.1%
96.6268 2
 
< 0.1%
95.4717 9
< 0.1%
94.5298 12
< 0.1%
93.4235 17
< 0.1%

Interactions

2022-12-20T14:00:17.961440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:08.863744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:12.442772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:16.187594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:19.912176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:23.533075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:27.167013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:30.782173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:34.250283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:37.878201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:41.701568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:45.219507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:48.767936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:52.414570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:56.201089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:59.859481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:03.367724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:07.069556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:10.826991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:14.306544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:18.133365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.053435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:12.631164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:16.399474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:20.075027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:23.786721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:27.338985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:30.942022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:34.421503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:38.039253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:41.870565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:45.386909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:48.943462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:52.579232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:56.375861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:00.027304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:03.537522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:07.305741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:10.994101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:14.461692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:18.318745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.208819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:12.801763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:16.601026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:20.261487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:23.974177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:27.530149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:31.129537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:34.612794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:38.227799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:42.058765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:45.573323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:49.138776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:52.772279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:56.565613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:00.218564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:03.732675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:07.498569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:11.185565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:14.704188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:18.489913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.358371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:12.968158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:16.770598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:20.438887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:24.149053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:27.702410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:31.294260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:34.790180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:38.401951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:42.233748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:45.738290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:49.319152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:53.040585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:56.746812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:00.391371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:03.913176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:07.673462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:11.358800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:14.891903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:18.678623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.515356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:13.145477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:16.947536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:20.614613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:24.328661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:27.878726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:31.479178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:34.971834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:38.587774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:42.417875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:45.918283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:49.510177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:53.228880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:56.939258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:00.575141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:04.093745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:07.857867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:11.542231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:15.078941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:18.861403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.670213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:13.323759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:17.160488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:20.788534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:24.498065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:28.059392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:31.652583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:35.147822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:38.762708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:42.596957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:46.101352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:49.695252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:53.414782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:57.122194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:00.750807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:04.278384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:08.033904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:11.716017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:15.257352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:19.052620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.825249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:13.499875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:17.351247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:20.966877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:24.681401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:28.235672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:31.836538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:35.420736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:38.943379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:42.777497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:46.278096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:49.887626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:53.605591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:57.309573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:00.937440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:04.471430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:08.219959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:11.903183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:15.438446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:19.222990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:09.983653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:13.664105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:17.521186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:21.138535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:24.856721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:28.413148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:32.009468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:35.599445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:39.118664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:42.951575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:46.451880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:50.070316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:53.798039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:57.494229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:01.106756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:04.735243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:08.401980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:12.081530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:15.612415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:19.401326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:10.138828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:13.846025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:17.696965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T13:59:19.593972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:23.188100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:26.830287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:30.433978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:33.915146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:37.521589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:41.319817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:44.876966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:48.430435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:52.066059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:55.843270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:59.522445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:03.028646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:06.709572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:10.386543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:13.970053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:17.627812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:21.299806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:12.070243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:15.993525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:19.750888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:23.357220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:26.997674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:30.597859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:34.074891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:37.691566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:41.525988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:45.042050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:48.596393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:52.234879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:56.019114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:59:59.685553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:03.193839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:06.887401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:10.651269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:14.135965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:00:17.794112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:00:31.160086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:00:31.436977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:00:31.716063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:00:31.996874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:00:32.276368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:00:21.590796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:00:22.699469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.054.625825.3178242.57240.203055.148372.563885.397440.510484.992276.981780.530262.538557.946068.144163.909062.219752.494364.3090
10.3100.052.630025.3000241.53260.202070.761977.677083.050740.713481.019282.250184.479167.778755.624166.083258.326064.986452.845365.2822
20.6200.052.630025.3000241.23150.202068.557177.677079.236941.418581.019281.516577.674462.538556.726662.309265.830762.219752.205264.8363
30.9290.052.630025.3000240.93150.201069.144875.034575.873438.185369.006070.439075.012066.068955.964164.659958.682559.601853.501067.8952
41.2370.052.630025.3000240.65210.200761.410069.144874.582538.402372.418164.358369.082962.014858.763063.212363.403958.020450.969564.8363
51.5460.052.630025.3000240.75720.200557.730462.686965.631834.444763.052462.931164.529266.068957.151265.098158.326060.500751.862466.2847
61.8570.052.630025.3000240.86290.201053.489859.294659.746233.809357.962156.007860.454862.981655.624160.577258.682557.672551.245365.8253
72.1640.052.630025.3000240.96730.200045.852554.153558.149832.898058.384753.488754.023158.423557.582065.631864.335956.188949.790063.8761
82.4740.052.630025.3000240.93080.200043.113849.511153.827331.333952.104351.125154.705366.662555.964160.577259.482057.672551.245364.3090
92.7840.052.630025.3000240.86170.201040.127045.852552.178630.804751.766150.836949.678758.041257.946062.309264.335955.092950.053363.3641
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29564390906.9450.063.0925.380.19940.210.48924.749918.026522.194341.777140.290741.931257.588857.151266.633065.830754.405945.784165.8253
29564490907.2550.063.0925.380.00000.28.62163.764514.931521.921840.805838.750942.767566.068955.964159.746259.927256.519547.062967.3181
29564590907.5640.063.0925.380.00000.27.09103.049812.475721.325839.478337.479141.342857.588855.624162.309266.283455.092946.552267.3181
29564690907.8740.063.0925.380.00000.25.84472.533710.410321.198838.766035.992341.126259.282453.478359.746265.295454.778546.278266.2847
29564790908.1820.063.0925.380.00000.24.78662.15318.714021.073340.046535.361639.240056.415857.582063.715962.989053.436145.300164.8363
29564890908.4910.063.0925.380.00000.24.01251.86547.254520.466537.414032.892838.374563.978652.136059.302358.326053.436144.153668.3837
29564990908.7990.063.0925.380.00000.23.36971.64966.081420.170437.925331.734538.032555.220053.105557.728964.335954.405945.562866.8445
29565090909.1070.063.0925.380.00000.22.87501.47825.188220.056338.957830.398836.407955.220051.781361.431263.403953.735544.153664.8363
29565190909.4170.063.0925.380.00000.22.46231.34444.399819.348537.090328.766436.099162.538550.242256.972557.556555.092944.613865.8253
29565290909.7270.063.0925.380.00000.22.14321.23543.807918.933536.944926.909034.072355.988053.478362.798762.498053.436143.260662.9436